Nature is the best source of anti-inflammatory drugs: indexing natural products for their anti-inflammatory bioactivity

Objectives The aim was to index natural products for less expensive preventive or curative anti-inflammatory therapeutic drugs. Materials A set of 441 anti-inflammatory drugs representing the active domain and 2892 natural products representing the inactive domain was used to construct a predictive model for bioactivity-indexing purposes. Method The model for indexing the natural products for potential anti-inflammatory activity was constructed using the iterative stochastic elimination algorithm (ISE). ISE is capable of differentiating between active and inactive antiinflammatory molecules. Results By applying the prediction model to a mix set of (active/inactive) substances, we managed to capture 38% of the anti-inflammatory drugs in the top 1% of the screened set of chemicals, yielding enrichment factor of 38. Ten natural products that scored highly as potential antiinflammatory drug candidates are disclosed. Searching the PubMed revealed that only three molecules (Moupinamide, Capsaicin, and Hypaphorine) out of the ten were tested and reported as anti-inflammatory. The other seven phytochemicals await evaluation for their anti-inflammatory activity in wet lab. Conclusion The proposed anti-inflammatory model can be utilized for the virtual screening of large chemical databases and for indexing natural products for potential antiinflammatory activity.This work was partially supported by the Al- Qasemi Research Foundation (Grant no. 954000) and the Ministry of Science, Space and Technology, Israel. We declare that the funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript

A Novel Paclitaxel Conjugate with Higher Efficiency and Lower Toxicity: A New Drug Candidate for Cancer Treatment

Paclitaxel-lipoate (IDD-1040) is a conjugate formed by the chemical joining of the two compounds, by condensing a lipoic acid moiety to the C2′ of paclitaxel. IDD-1040 was evaluated for its anti-tumor activity and potential druggability, using an in vivo non-small-cell, lung cancer (NSCLC) xenograft mouse model. In the in vivo studies, IDD-1040 showed a maximum tolerated dose (MTD) of 250 mg/kg compared to paclitaxel (PTX), with an MTD of 20 mg/kg. Most interesting, IDD-1040 demonstrated higher anti-tumor activity, and its inhibitory activity on tumor volume (cell growth) was dose-dependent. That anti-tumor activity persisted for two weeks after cessation of IDD-1040 treatment, as opposed to PTX cessation, after which the tumor relapsed, confirming that IDD-1040 exhibits superior tumor inhibition. Similar to PTX treatment, no marked body weight decrease was observed during IDD-1040 treatment, indicating a low toxicity profile. The increase in animal body weight noted over time was due to the increasing weight of tumors, recorded in all the mouse test groups. The results also showed that mortality rate of mice was reduced by treatment with IDD-1040, more so than with PTX. Furthermore, in a preliminary study on the ex vivo distribution of IDD-1040, neutropenia was primarily concentrated in the liver 1 h after injection, and most of the drug was metabolized by the liver in 24 h. All of these results demonstrate IDD-1040’s great potential as a candidate drug for cancer treatment

Twelve of the natural products that are scored highly as potential anticancer drug candidates according to our ISE-based anticancer indexing model.

<p>Twelve of the natural products that are scored highly as potential anticancer drug candidates according to our ISE-based anticancer indexing model.</p

A receiver operating characteristic (ROC) curve showing the performance of the anticancer bioactivity-indexing model.

<p>A receiver operating characteristic (ROC) curve showing the performance of the anticancer bioactivity-indexing model.</p

Enrichment plot of the anticancer potential activity-indexing model of natural products.

<p>Enrichment plot of the anticancer potential activity-indexing model of natural products.</p

Nature is the best source of anticancer drugs: Indexing natural products for their anticancer bioactivity

<div><p>Cancer is considered one of the primary diseases that cause morbidity and mortality in millions of people worldwide and due to its prevalence, there is undoubtedly an unmet need to discover novel anticancer drugs. However, the traditional process of drug discovery and development is lengthy and expensive, so the application of <i>in silico</i> techniques and optimization algorithms in drug discovery projects can provide a solution, saving time and costs. A set of 617 approved anticancer drugs, constituting the active domain, and a set of 2,892 natural products, constituting the inactive domain, were employed to build predictive models and to index natural products for their anticancer bioactivity. Using the iterative stochastic elimination optimization technique, we obtained a highly discriminative and robust model, with an area under the curve of 0.95. Twelve natural products that scored highly as potential anticancer drug candidates are disclosed. Searching the scientific literature revealed that few of those molecules (Neoechinulin, Colchicine, and Piperolactam) have already been experimentally screened for their anticancer activity and found active. The other phytochemicals await evaluation for their anticancerous activity in wet lab.</p></div

Physicochemical properties distribution of anticancer drugs (A) Molecular weight distribution, (B) Log P values, (C) Number of H-bond acceptors [lip_acc], (D) Number of H-bond donors [lip_don], (E) Number of rigid bonds, (F) number of rotatable bonds, (G) Number of aromatic atoms.

<p>Physicochemical properties distribution of anticancer drugs (A) Molecular weight distribution, (B) Log P values, (C) Number of H-bond acceptors [lip_acc], (D) Number of H-bond donors [lip_don], (E) Number of rigid bonds, (F) number of rotatable bonds, (G) Number of aromatic atoms.</p

Violation distribution of anticancer drugs to Lipinski rule of 5 for drug-likeness (left side) and Oprea rule for lead-likeness (right side).

<p>Violation distribution of anticancer drugs to Lipinski rule of 5 for drug-likeness (left side) and Oprea rule for lead-likeness (right side).</p

Redundancy of descriptors in the 29 filters used to produce the anticancer indexing model.

<p>The picture was constructed by using WORDLE module.</p